cloudflare-vectorize

name	cloudflare-vectorize
description	Build semantic search with Cloudflare Vectorize V2. Covers async mutations, 5M vectors/index, 31ms latency, returnMetadata enum changes, and V1 deprecation. Prevents 14 errors including dimension mismatches, TypeScript types, testing setup. Use when: building RAG or semantic search, troubleshooting returnMetadata, V2 timing, metadata index, dimension errors, vitest setup, or wrangler --json output.
user-invocable	true

Cloudflare Vectorize

Complete implementation guide for Cloudflare Vectorize - a globally distributed vector database for building semantic search, RAG (Retrieval Augmented Generation), and AI-powered applications with Cloudflare Workers.

Status: Production Ready ✅ Last Updated: 2026-01-21 Dependencies: cloudflare-worker-base (for Worker setup), cloudflare-workers-ai (for embeddings) Latest Versions: wrangler@4.59.3, @cloudflare/workers-types@4.20260109.0 Token Savings: ~70% Errors Prevented: 14 Dev Time Saved: ~4 hours

What This Skill Provides

Core Capabilities

• ✅ Index Management: Create, configure, and manage vector indexes
• ✅ Vector Operations: Insert, upsert, query, delete, and list vectors (list-vectors added August 2025)
• ✅ Metadata Filtering: Advanced filtering with 10 metadata indexes per index
• ✅ Semantic Search: Find similar vectors using cosine, euclidean, or dot-product metrics
• ✅ RAG Patterns: Complete retrieval-augmented generation workflows
• ✅ Workers AI Integration: Native embedding generation with @cf/baai/bge-base-en-v1.5
• ✅ OpenAI Integration: Support for text-embedding-3-small/large models
• ✅ Document Processing: Text chunking and batch ingestion pipelines
• ✅ Testing Setup: Vitest configuration with Vectorize bindings

Templates Included

1. basic-search.ts - Simple vector search with Workers AI
2. rag-chat.ts - Full RAG chatbot with context retrieval
3. document-ingestion.ts - Document chunking and embedding pipeline
4. metadata-filtering.ts - Advanced filtering patterns

---

⚠️ Vectorize V2 Breaking Changes (September 2024)

IMPORTANT: Vectorize V2 became GA in September 2024 with significant breaking changes.

What Changed in V2

Performance Improvements:

• Index capacity: 200,000 → 5 million vectors per index
• Query latency: 549ms → 31ms median (18× faster)
• TopK limit: 20 → 100 results per query
• Scale limits: 100 → 50,000 indexes per account
• Namespace limits: 100 → 50,000 namespaces per index

Breaking API Changes:

1. Async Mutations - All mutations now asynchronous:

   // V2: Returns mutationId
   const result = await env.VECTORIZE_INDEX.insert(vectors);
   console.log(result.mutationId); // "xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx"
   
   // Vector inserts/deletes may take a few seconds to be reflected
   

2. returnMetadata Parameter - Boolean → String enum:

   // ❌ V1 (deprecated)
   { returnMetadata: true }
   
   // ✅ V2 (required)
   { returnMetadata: 'all' | 'indexed' | 'none' }
   

3. Metadata Indexes Required Before Insert:

   • V2 requires metadata indexes created BEFORE vectors inserted
   • Vectors added before metadata index won't be indexed
   • Must re-upsert vectors after creating metadata index

V1 Deprecation Timeline:

• December 2024: Can no longer create V1 indexes
• Existing V1 indexes: Continue to work (other operations unaffected)
• Migration: Use wrangler vectorize --deprecated-v1 flag for V1 operations

Wrangler Version Required:

• Minimum: wrangler@3.71.0 for V2 commands
• Recommended: wrangler@4.54.0+ (latest)

Check Mutation Status

// Get index info to check last mutation processed
const info = await env.VECTORIZE_INDEX.describe();
console.log(info.mutationId); // Last mutation ID
console.log(info.processedUpToMutation); // Last processed timestamp

---

Critical Setup Rules

⚠️ MUST DO BEFORE INSERTING VECTORS

# 1. Create the index with FIXED dimensions and metric
npx wrangler vectorize create my-index \
  --dimensions=768 \
  --metric=cosine

# 2. Create metadata indexes IMMEDIATELY (before inserting vectors!)
npx wrangler vectorize create-metadata-index my-index \
  --property-name=category \
  --type=string

npx wrangler vectorize create-metadata-index my-index \
  --property-name=timestamp \
  --type=number

Why: Metadata indexes MUST exist before vectors are inserted. Vectors added before a metadata index was created won't be filterable on that property.

Index Configuration (Cannot Be Changed Later)

# Dimensions MUST match your embedding model output:
# - Workers AI @cf/baai/bge-base-en-v1.5: 768 dimensions
# - OpenAI text-embedding-3-small: 1536 dimensions
# - OpenAI text-embedding-3-large: 3072 dimensions

# Metrics determine similarity calculation:
# - cosine: Best for normalized embeddings (most common)
# - euclidean: Absolute distance between vectors
# - dot-product: For non-normalized vectors

Wrangler Configuration

wrangler.jsonc:

{
  "name": "my-vectorize-worker",
  "main": "src/index.ts",
  "compatibility_date": "2025-10-21",
  "vectorize": [
    {
      "binding": "VECTORIZE_INDEX",
      "index_name": "my-index"
    }
  ],
  "ai": {
    "binding": "AI"
  }
}

TypeScript Types

export interface Env {
  VECTORIZE_INDEX: VectorizeIndex;
  AI: Ai;
}

interface VectorizeVector {
  id: string;
  values: number[] | Float32Array | Float64Array;
  namespace?: string;
  metadata?: Record<string, string | number | boolean | string[]>;
}

interface VectorizeMatches {
  matches: Array<{
    id: string;
    score: number;
    values?: number[];
    metadata?: Record<string, any>;
    namespace?: string;
  }>;
  count: number;
}

Metadata Filter Operators (V2)

Vectorize V2 supports advanced metadata filtering with range queries:

// Equality (implicit $eq)
{ category: "docs" }

// Not equals
{ status: { $ne: "archived" } }

// In/Not in arrays
{ category: { $in: ["docs", "tutorials"] } }
{ category: { $nin: ["deprecated", "draft"] } }

// Range queries (numbers) - NEW in V2
{ timestamp: { $gte: 1704067200, $lt: 1735689600 } }

// Range queries (strings) - prefix searching
{ url: { $gte: "/docs/workers", $lt: "/docs/workersz" } }

// Nested metadata with dot notation
{ "author.id": "user123" }

// Multiple conditions (implicit AND)
{ category: "docs", language: "en", "metadata.published": true }

Metadata Best Practices

1. Cardinality Considerations

Low Cardinality (Good for $eq filters):

// Few unique values - efficient filtering
metadata: {
  category: "docs",        // ~10 categories
  language: "en",          // ~5 languages
  published: true          // 2 values (boolean)
}

High Cardinality (Avoid in range queries):

// Many unique values - avoid large range scans
metadata: {
  user_id: "uuid-v4...",         // Millions of unique values
  timestamp_ms: 1704067200123    // Use seconds instead
}

2. Metadata Limits

• Max 10 metadata indexes per Vectorize index
• Max 10 KiB metadata per vector
• String indexes: First 64 bytes (UTF-8)
• Number indexes: Float64 precision
• Filter size: Max 2048 bytes (compact JSON)

3. Vector Dimension Limit

Current Limit: 1536 dimensions per vector Source: GitHub Issue #8729

Supported Embedding Models:

• Workers AI @cf/baai/bge-base-en-v1.5: 768 dimensions ✅
• OpenAI text-embedding-3-small: 1536 dimensions ✅
• OpenAI text-embedding-3-large: 3072 dimensions ❌ (requires dimension reduction)

Unsupported Models (>1536 dimensions):

• nomic-embed-code: 3584 dimensions
• Qodo-Embed-1-7B: >1536 dimensions

Workaround: Use dimensionality reduction (e.g., PCA) to compress embeddings to 1536 or fewer dimensions, though this may reduce semantic quality.

Feature Request: Higher dimension support is under consideration. Use Limit Increase Request Form if this blocks your use case.

4. Key Restrictions

// ❌ INVALID metadata keys
metadata: {
  "": "value",              // Empty key
  "user.name": "John",      // Contains dot (reserved for nesting)
  "$admin": true,           // Starts with $
  "key\"with\"quotes": 1    // Contains quotes
}

// ✅ VALID metadata keys
metadata: {
  "user_name": "John",
  "isAdmin": true,
  "nested": { "allowed": true }  // Access as "nested.allowed" in filters
}

Best Practices

Batch Insert Performance

Critical: Use batch size of 5000 vectors for optimal performance.

Performance Data:

• Individual inserts: 2.5M vectors in 36+ hours (incomplete)
• Batch inserts (5000): 4M vectors in ~12 hours
• 18× faster with proper batching

Why 5000?

• Vectorize's internal Write-Ahead Log (WAL) optimized for this size
• Avoids Cloudflare API rate limits
• Balances throughput and memory usage

Optimal Pattern:

const BATCH_SIZE = 5000;

async function insertVectors(vectors: VectorizeVector[]) {
  for (let i = 0; i < vectors.length; i += BATCH_SIZE) {
    const batch = vectors.slice(i, i + BATCH_SIZE);
    const result = await env.VECTORIZE.insert(batch);
    console.log(`Inserted batch ${i / BATCH_SIZE + 1}, mutationId: ${result.mutationId}`);

    // Optional: Rate limiting delay
    if (i + BATCH_SIZE < vectors.length) {
      await new Promise(resolve => setTimeout(resolve, 100));
    }
  }
}

Sources:

• Community Report
• Official Best Practices

---

Query Accuracy Modes

Vectorize uses approximate nearest neighbor (ANN) search by default with ~80% accuracy compared to exact search.

Default Mode: Approximate scoring (~80% accuracy)

• Faster latency
• Good for RAG, search, recommendations
• topK up to 100

High-Precision Mode: Near 100% accuracy

• Enabled via returnValues: true
• Higher latency
• Limited to topK=20

Trade-off Example:

// Fast, ~80% accuracy, topK up to 100
const results = await env.VECTORIZE.query(embedding, {
  topK: 50,
  returnValues: false  // Default
});

// Slower, ~100% accuracy, topK max 20
const preciseResults = await env.VECTORIZE.query(embedding, {
  topK: 10,
  returnValues: true   // High-precision scoring
});

When to Use High-Precision:

• Critical applications (fraud detection, legal compliance)
• Small result sets (topK < 20)
• Accuracy is higher priority than latency

Source: Cloudflare Blog - Building Vectorize

---

Common Errors & Solutions

Error 1: Metadata Index Created After Vectors Inserted

Problem: Filtering doesn't work on existing vectors
Solution: Delete and re-insert vectors OR create metadata indexes BEFORE inserting

Error 2: Dimension Mismatch

Problem: "Vector dimensions do not match index configuration"
Solution: Ensure embedding model output matches index dimensions:
  - Workers AI bge-base: 768
  - OpenAI small: 1536
  - OpenAI large: 3072

Error 3: Invalid Metadata Keys

Problem: "Invalid metadata key"
Solution: Keys cannot:
  - Be empty
  - Contain . (dot)
  - Contain " (quote)
  - Start with $ (dollar sign)

Error 4: Filter Too Large

Problem: "Filter exceeds 2048 bytes"
Solution: Simplify filter or split into multiple queries

Error 5: Range Query on High Cardinality

Problem: Slow queries or reduced accuracy
Solution: Use lower cardinality fields for range queries, or use seconds instead of milliseconds for timestamps

Error 6: Insert vs Upsert Confusion

Problem: Updates not reflecting in index
Solution: Use upsert() to overwrite existing vectors, not insert()

Error 7: Missing Bindings

Problem: "VECTORIZE_INDEX is not defined"
Solution: Add [[vectorize]] binding to wrangler.jsonc

Error 8: Namespace vs Metadata Confusion

Problem: Unclear when to use namespace vs metadata filtering
Solution:
  - Namespace: Partition key, applied BEFORE metadata filters
  - Metadata: Flexible key-value filtering within namespace

Error 9: V2 Async Mutation Timing (NEW in V2)

Problem: Inserted vectors not immediately queryable
Solution: V2 mutations are asynchronous - vectors may take a few seconds to be reflected
  - Use mutationId to track mutation status
  - Check env.VECTORIZE_INDEX.describe() for processedUpToMutation timestamp

Error 10: V1 returnMetadata Boolean (BREAKING in V2)

Problem: "returnMetadata must be 'all', 'indexed', or 'none'"
Solution: V2 changed returnMetadata from boolean to string enum:
  - ❌ V1: { returnMetadata: true }
  - ✅ V2: { returnMetadata: 'all' }

Error 11: Wrangler --json Output Contains Log Prefix

Error: wrangler vectorize list --json output starts with log message, breaking JSON parsing Source: GitHub Issue #11011

Affected Commands:

• wrangler vectorize list --json
• wrangler vectorize list-metadata-index --json

Problem:

$ wrangler vectorize list --json
📋 Listing Vectorize indexes...
[
  { "created_on": "2025-10-18T13:28:30.259277Z", ... }
]

The log message makes output invalid JSON, breaking piping to jq or other tools.

Solution: Strip first line before parsing:

# Using tail
wrangler vectorize list --json | tail -n +2 | jq '.'

# Using sed
wrangler vectorize list --json | sed '1d' | jq '.'

---

Error 12: TypeScript Types Missing Filter Operators

Error: wrangler types generates incomplete VectorizeVectorMetadataFilterOp type Source: GitHub Issue #10092 Status: OPEN (tracked internally as VS-461)

Problem: Generated type only includes $eq and $ne, missing V2 operators: $in, $nin, $lt, $lte, $gt, $gte

Impact: TypeScript shows false errors when using valid V2 metadata filter operators:

const vectorizeRes = env.VECTORIZE.queryById(imgId, {
  filter: { gender: { $in: genderFilters } }, // ❌ TS error but works!
  topK,
  returnMetadata: 'indexed',
});

Workaround: Manual type override until wrangler types is fixed:

// Add to your types file
type VectorizeMetadataFilter = Record<string,
  | string
  | number
  | boolean
  | {
      $eq?: string | number | boolean;
      $ne?: string | number | boolean;
      $in?: (string | number | boolean)[];
      $nin?: (string | number | boolean)[];
      $lt?: number | string;
      $lte?: number | string;
      $gt?: number | string;
      $gte?: number | string;
    }
>;

---

Error 13: Windows Dev Registry Failure (FIXED)

Error: ENOENT: no such file or directory when running wrangler dev on Windows Source: GitHub Issue #10383 Status: FIXED in wrangler@4.32.0

Problem: Wrangler attempted to create external worker files with colons in the name (invalid on Windows):

Error: ENOENT: ... '__WRANGLER_EXTERNAL_VECTORIZE_WORKER:<project>:<binding>'

Solution: Update to wrangler@4.32.0 or later:

npm install -g wrangler@latest

---

Error 14: topK Limit Depends on returnValues/returnMetadata

Error: topK exceeds maximum allowed value Source: Vectorize Limits

Problem: Maximum topK value changes based on query options:

Configuration	Max topK
returnValues: false, returnMetadata: 'none'	100
returnValues: true OR returnMetadata: 'all'	20
returnMetadata: 'indexed'	100

Common Error:

// ❌ ERROR - topK too high with returnValues
query(embedding, {
  topK: 100,            // Exceeds limit!
  returnValues: true    // Max topK=20 when true
});

Solution:

// ✅ OK - respects conditional limit
query(embedding, {
  topK: 20,
  returnValues: true
});

// ✅ OK - higher topK without values
query(embedding, {
  topK: 100,
  returnValues: false,
  returnMetadata: 'indexed'
});

---

V2 Migration Checklist

If migrating from V1 to V2:

1. ✅ Update wrangler to 3.71.0+ (npm install -g wrangler@latest)
2. ✅ Create new V2 index (can't upgrade V1 → V2)
3. ✅ Create metadata indexes BEFORE inserting vectors
4. ✅ Update returnMetadata boolean → string enum ('all', 'indexed', 'none')
5. ✅ Handle async mutations (expect mutationId in responses)
6. ✅ Test with V2 limits (topK up to 100, 5M vectors per index)
7. ✅ Update error handling for async behavior

V1 Deprecation:

• After December 2024: Cannot create new V1 indexes
• Existing V1 indexes: Continue to work
• Use wrangler vectorize --deprecated-v1 for V1 operations

---

Testing Considerations

Vitest with Vectorize Bindings

Issue: Using @cloudflare/vitest-pool-workers with Vectorize or Workers AI bindings causes runtime failure. Source: GitHub Issue #7434

Error: wrapped binding module can't be resolved

Workaround:

1. Create wrangler-test.jsonc without Vectorize/AI bindings
2. Point vitest config to test-specific wrangler file
3. Mock bindings in your tests

Example:

// wrangler-test.jsonc (no Vectorize binding)
{
  "name": "my-worker-test",
  "main": "src/index.ts",
  "compatibility_date": "2025-10-21"
  // No vectorize binding
}

// vitest.config.ts
import { defineWorkersProject } from '@cloudflare/vitest-pool-workers/config';

export default defineWorkersProject({
  test: {
    poolOptions: {
      workers: {
        wrangler: {
          configPath: "./wrangler-test.jsonc"
        }
      }
    }
  }
});

// Mock in tests
import { vi } from 'vitest';

const mockVectorize = {
  query: vi.fn().mockResolvedValue({
    matches: [
      { id: 'test-1', score: 0.95, metadata: { category: 'docs' } }
    ],
    count: 1
  }),
  insert: vi.fn().mockResolvedValue({ mutationId: "test-mutation-id" }),
  upsert: vi.fn().mockResolvedValue({ mutationId: "test-mutation-id" })
};


(,  () => {
   env = { : mockVectorize };
  
});

---

Community Tips

Note: These tips come from community discussions and official blog posts. Verify against your Vectorize version.

Tip 1: Range Queries at Scale May Have Reduced Accuracy (Community-sourced)

Source: Query Best Practices Confidence: MEDIUM Applies to: Datasets with ~10M+ vectors

Range queries ($lt, $lte, $gt, $gte) on large datasets may experience reduced accuracy.

Optimization Strategy:

// ❌ High-cardinality range at scale
metadata: {
  timestamp_ms: 1704067200123
}
filter: { timestamp_ms: { $gte: 1704067200000 } }

// ✅ Bucketed into discrete values
metadata: {
  timestamp_bucket: "2025-01-01-00:00",  // 1-hour buckets
  timestamp_ms: 1704067200123  // Original (non-indexed)
}
filter: {
  timestamp_bucket: {
    $in: ["2025-01-01-00:00", "2025-01-01-01:00"]
  }
}

When This Matters:

• Time-based filtering over months/years
• User IDs, transaction IDs (UUID ranges)
• Any high-cardinality continuous data

Alternative: Use equality filters ($eq, $in) with bucketed values.

---

Tip 2: List Vectors Operation (Added August 2025)

Source: Vectorize Changelog

Vectorize V2 added support for the list-vectors operation for paginated iteration through vector IDs.

Use Cases:

• Auditing vector collections
• Bulk vector operations
• Debugging index contents

API:

const result = await env.VECTORIZE_INDEX.list({
  limit: 1000,  // Max 1000 per page
  cursor?: string
});

// result.vectors: Array<{ id: string }>
// result.cursor: string | undefined
// result.count: number

// Pagination example
let cursor: string | undefined;
const allVectorIds: string[] = [];

do {
  const result = await env.VECTORIZE_INDEX.list({
    limit: 1000,
    cursor
  });
  allVectorIds.push(...result.vectors.map(v => v.id));
  cursor = result.cursor;
} while (cursor);

Limitations:

• Returns IDs only (not values or metadata)
• Max 1000 vectors per page
• Use cursor for pagination

---

Official Documentation

• Vectorize V2 Docs: https://developers.cloudflare.com/vectorize/
• V2 Changelog: https://developers.cloudflare.com/vectorize/platform/changelog/
• V1 to V2 Migration: https://developers.cloudflare.com/vectorize/reference/transition-vectorize-legacy/
• Metadata Filtering: https://developers.cloudflare.com/vectorize/reference/metadata-filtering/
• Workers AI Models: https://developers.cloudflare.com/workers-ai/models/

---

Status: Production Ready ✅ (Vectorize V2 GA - September 2024) Last Updated: 2026-01-21 Token Savings: ~70% Errors Prevented: 14 (includes V2 breaking changes, testing setup, TypeScript types) Changes: Added 4 new errors (wrangler --json, TypeScript types, Windows dev, topK limits), batch performance best practices, query accuracy modes, testing setup, community tips on range queries and list-vectors operation.